OpenRaiser/Intern-Atlas huggingface dataset:
The Affect of Software Developers: Common Misconceptions and Measurements
Venue: arXiv cs.SE 2015
Year: 2015

The study of affects (i.e., emotions, moods) in the workplace has received a lot of attention in the last 15 years. Despite the fact that software development has been shown to be intellectual, creative, and driven by cognitive activities, and that affects have a deep influence on cognitive activities, software engineering research lacks an understanding of the affects of software developers. This note provides (1) common misconceptions of affects when dealing with job satisfaction, motivation, commitment, well-being, and happiness; (2) validated measurement instruments for affect measurement; and (3) our recommendations when measuring the affects of software developers.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Venue: arXiv cs.CV 2015
Year: 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
Predicting Important Objects for Egocentric Video Summarization
Venue: arXiv cs.CV 2015
Year: 2015

We present a video summarization approach for egocentric or "wearable" camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video---such as the nearness to hands, gaze, and frequency of occurrence---and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. We adjust the compactness of the final summary given either an importance selection criterion or a length budget; for the latter, we design an efficient dynamic programming solution that accounts for importance, visual uniqueness, and temporal displacement. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results on two egocentric video datasets show the method's promise relative to existing techniques for saliency and summarization.
Deciding definability in FO2(<h,<v) on trees
Venue: arXiv cs.LO 2015
Year: 2015

We provide a decidable characterization of regular forest languages definable in FO2(<h,<v). By FO2(<h,<v) we refer to the two variable fragment of first order logic built from the descendant relation and the following sibling relation. In terms of expressive power it corresponds to a fragment of the navigational core of XPath that contains modalities for going up to some ancestor, down to some descendant, left to some preceding sibling, and right to some following sibling. We also show that our techniques can be applied to other two variable first-order logics having exactly the same vertical modalities as FO2(<h,<v) but having different horizontal modalities.
Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms
Venue: arXiv cs.DC 2015
Year: 2015

The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy. Recently, several parallelization approaches have been proposed in order to scale SGD to solve very large ML problems. At their core, most of these approaches are following a map-reduce scheme. This paper presents a novel parallel updating algorithm for SGD, which utilizes the asynchronous single-sided communication paradigm. Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent (ASGD) provides faster (or at least equal) convergence, close to linear scaling and stable accuracy.
Low Complexity Belief Propagation Polar Code Decoders
Venue: arXiv cs.IT 2015
Year: 2015

Since its invention, polar code has received a lot of attention because of its capacity-achieving performance and low encoding and decoding complexity. Successive cancellation decoding (SCD) and belief propagation decoding (BPD) are two of the most popular approaches for decoding polar codes. SCD is able to achieve good error-correcting performance and is less computationally expensive as compared to BPD. However SCDs suffer from long latency and low throughput due to the serial nature of the successive cancellation algorithm. BPD is parallel in nature and hence is more attractive for high throughput applications. However since it is iterative in nature, the required latency and energy dissipation increases linearly with the number of iterations. In this work, we borrow the idea of SCD and propose a novel scheme based on sub-factor-graph freezing to reduce the average number of computations as well as the average number of iterations required by BPD, which directly translates into lower latency and energy dissipation. Simulation results show that the proposed scheme has no performance degradation and achieves significant reduction in computation complexity over the existing methods.
Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes
Venue: arXiv cs.AI 2015
Year: 2015

This paper introduces the necessary and sufficient conditions that surrogate functions must satisfy to properly define frontiers of non-dominated solutions in multi-objective optimization problems. These new conditions work directly on the objective space, thus being agnostic about how the solutions are evaluated. Therefore, real objectives or user-designed objectives' surrogates are allowed, opening the possibility of linking independent objective surrogates. To illustrate the practical consequences of adopting the proposed conditions, we use Gaussian processes as surrogates endowed with monotonicity soft constraints and with an adjustable degree of flexibility, and compare them to regular Gaussian processes and to a frontier surrogate method in the literature that is the closest to the method proposed in this paper. Results show that the necessary and sufficient conditions proposed here are finely managed by the constrained Gaussian process, guiding to high-quality surrogates capable of suitably synthesizing an approximation to the Pareto frontier in challenging instances of multi-objective optimization, while an existing approach that does not take the theory proposed in consideration defines surrogates which greatly violate the conditions to describe a valid frontier.
Multiparty Probability Computation and Verification
Venue: arXiv cs.CR 2015
Year: 2015

A multiparty computation protocol is described in which the parties can generate different probability events that is based on the sharing of a single anonymized random number, and also perform oblivious transfer. A method to verify the correctness of the procedure, without revealing the random numbers used by the parties, is proposed.
Learning with a Drifting Target Concept
Venue: arXiv cs.LG 2015
Year: 2015

We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.
The key properties of inconsistency indicators for a triad in pairwise comparison matrices
Venue: arXiv cs.DM 2015
Year: 2015

Processing information, acquired by subjective assessments, involves inconsistency analysis in most (if not all) applications of which some are of considerable importance at a national level (see, Koczkodaj/Kulakowski/Ligenza, Scientometrics, 99(3): 911-926, 2014)A triad inconsistency axiomatization in pairwise comparisons was informally proposed in Koczkodaj/Szwarc, FUNDAMENTA INFORMATICAE, 132(4): 485-500, 2014. This study, rectifies it by the use of the distance and theoretical proofs. Three key properties of the indicator are presented in this study and illustrated by several examples.
Modelling and Verifying an Object-Oriented Concurrency Model in GROOVE
Venue: arXiv cs.PL 2015
Year: 2015

SCOOP is a programming model and language that allows concurrent programming at a high level of abstraction. Several approaches to verifying SCOOP programs have been proposed in the past, but none of them operate directly on the source code without modifications or annotations. We propose a fully automatic approach to verifying (a subset of) SCOOP programs by translation to graph-based models. First, we present a graph transformation based semantics for SCOOP. We present an implementation of the model in the state-of-the-art model checker GROOVE, which can be used to simulate programs and verify concurrency and consistency properties, such as the impossibility of deadlocks occurring or the absence of postcondition violations. Second, we present a translation tool that operates on SCOOP program code and generates input for the model. We evaluate our approach by inspecting a number of programs in the form of case studies.
Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames
Venue: arXiv cs.IT 2015
Year: 2015

Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of (Adcock, Hansen, Poon and Roman, arXiv:1302.0561, 2013) to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities -- the sparsity in each level under the sparsifying operator.
Bitcoin Blockchain Dynamics: the Selfish-Mine Strategy in the Presence of Propagation Delay
Venue: arXiv cs.CR 2015
Year: 2015

In the context of the `selfish-mine' strategy proposed by Eyal and Sirer, we study the effect of propagation delay on the evolution of the Bitcoin blockchain. First, we use a simplified Markov model that tracks the contrasting states of belief about the blockchain of a small pool of miners and the `rest of the community' to establish that the use of block-hiding strategies, such as selfish-mine, causes the rate of production of orphan blocks to increase. Then we use a spatial Poisson process model to study values of Eyal and Sirer's parameter $γ$, which denotes the proportion of the honest community that mine on a previously-secret block released by the pool in response to the mining of a block by the honest community. Finally, we use discrete-event simulation to study the behaviour of a network of Bitcoin miners, a proportion of which is colluding in using the selfish-mine strategy, under the assumption that there is a propagation delay in the communication of information between miners.
Asynchronous Multi-Context Systems
Venue: arXiv cs.AI 2015
Year: 2015

In this work, we present asynchronous multi-context systems (aMCSs), which provide a framework for loosely coupling different knowledge representation formalisms that allows for online reasoning in a dynamic environment. Systems of this kind may interact with the outside world via input and output streams and may therefore react to a continuous flow of external information. In contrast to recent proposals, contexts in an aMCS communicate with each other in an asynchronous way which fits the needs of many application domains and is beneficial for scalability. The federal semantics of aMCSs renders our framework an integration approach rather than a knowledge representation formalism itself. We illustrate the introduced concepts by means of an example scenario dealing with rescue services. In addition, we compare aMCSs to reactive multi-context systems and describe how to simulate the latter with our novel approach.
Compressive Sensing of Large-Scale Images: An Assumption-Free Approach
Venue: arXiv cs.MM 2015
Year: 2015

Cost-efficient compressive sensing of big media data with fast reconstructed high-quality results is very challenging. In this paper, we propose a new large-scale image compressive sensing method, composed of operator-based strategy in the context of fixed point continuation method and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via simulations and comparisons with state-of-the-art algorithms.
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Venue: arXiv cs.CV 2015
Year: 2015

Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists that are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.
Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm
Venue: arXiv cs.CV 2015
Year: 2015

Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells. An unsupervised cell segmentation approach is presented here. Cell clump segmentation was carried out using the extended depth of field (EDF) image created from the images of different focal planes. A modified Otsu method with prior class weights is proposed for accurate segmentation of nuclei from the cell clumps. The cell cytoplasm was further segmented from cell clump depending upon the number of nucleus detected in that cell clump. Level set model was used for cytoplasm segmentation.
Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach
Venue: arXiv cs.SI 2015
Year: 2015

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users' interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.
Pilot Decontamination Through Pilot Sequence Hopping in Massive MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

This work concerns wireless cellular networks applying massive multiple-input multiple-output (MIMO) technology. In such a system, the base station in a given cell is equipped with a very large number (hundreds or even thousands) of antennas and serves multiple users. Estimation of the channel from the base station to each user is performed at the base station using an uplink pilot sequence. Such a channel estimation procedure suffers from pilot contamination. Orthogonal pilot sequences are used in a given cell but, due to the shortage of orthogonal sequences, the same pilot sequences must be reused in neighboring cells, causing pilot contamination. The solution presented in this paper suppresses pilot contamination, without the need for coordination among cells. Pilot sequence hopping is performed at each transmission slot, which provides a randomization of the pilot contamination. Using a modified Kalman filter, it is shown that such randomized contamination can be significantly suppressed. Comparisons with conventional estimation methods show that the mean squared error can be lowered as much as an order of magnitude at low mobility.
Massive MIMO for Crowd Scenarios: A Solution Based on Random Access
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents a new approach to intra-cell pilot contamination in crowded massive MIMO scenarios. The approach relies on two essential properties of a massive MIMO system, namely near-orthogonality between user channels and near-stability of channel powers. Signal processing techniques that take advantage of these properties allow us to view a set of contaminated pilot signals as a graph code on which iterative belief propagation can be performed. This makes it possible to decontaminate pilot signals and increase the throughput of the system. The proposed solution exhibits high performance with large improvements over the conventional method. The improvements come at the price of an increased error rate, although this effect is shown to decrease significantly for increasing number of antennas at the base station.
Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
Venue: arXiv cs.RO 2015
Year: 2015

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement.
A sparse recovery method for initial ranging in IEEE 802.16 OFDMA systems
Venue: arXiv cs.IT 2015
Year: 2015

Initial ranging constitutes a part of the synchronization procedure employed by the wireless communication standards. This allows the base station (BS) to detect the subscriber stations (SS) that are willing to commence communication. In addition, the ranging process allows the BS to estimate the uplink channel parameters of these SSs. Accurate estimation of these parameters are crucial as they ensure that the uplink signals from all the SSs arrive at the BS synchronously and approximately at the same power level. However, this detection and estimation problem turns out to be very challenging when multiple users initiate the ranging procedure at the same time. We address this issue by exploiting the underlying sparsity of the estimation problem. We propose a fast sparse signal recovery approach to improve the ranging performance in multi-user environment. Compared to the standard correlation based techniques, our method shows a clear improvement in ranging code detection, timing offset and channel power estimation. Although this method has been developed around the WiMAX standard, the underlying principles apply to other OFDM based standards as well.
CCS: It's not Fair! Fair Schedulers cannot be implemented in CCS-like languages even under progress and certain fairness assumptions
Venue: arXiv cs.LO 2015
Year: 2015

In the process algebra community it is sometimes suggested that, on some level of abstraction, any distributed system can be modelled in standard process-algebraic specification formalisms like CCS. This sentiment is strengthened by results testifying that CCS, like many similar formalisms, is Turing powerful and provides a mechanism for interaction. This paper counters that sentiment by presenting a simple fair scheduler---one that in suitable variations occurs in many distributed systems---of which no implementation can be expressed in CCS, unless CCS is enriched with a fairness assumption. Since Dekker's and Peterson's mutual exclusion protocols implement fair schedulers, it follows that these protocols cannot be rendered correctly in CCS without imposing a fairness assumption. Peterson expressed this algorithm correctly in pseudocode without resorting to a fairness assumption, so it furthermore follows that CCS lacks the expressive power to accurately capture such pseudocode.